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Due to the unstructuredness and the lack of schemas of graphs, such asknowledge graphs, social networks, and RDF graphs, keyword search for queryingsuch graphs has been proposed. As graphs have become voluminous, large-scaledistributed processing has attracted much interest from the database researchcommunity. While there have been several distributed systems, distributedquerying techniques for keyword search are still limited. This paper proposes anovel distributed keyword search system called $\DKWS$. First, we\revise{present} a {\em monotonic} property with keyword search algorithms thatguarantees correct parallelization. Second, we present a keyword searchalgorithm as monotonic backward and forward search phases. Moreover, we proposenew tight bounds for pruning nodes being searched. Third, we propose a {\emnotify-push} paradigm and $\PINE$ {\em programming model} of $\DKWS$. Thenotify-push paradigm allows {\em asynchronously} exchanging the upper bounds ofmatches across the workers and the coordinator in $\DKWS$. The $\PINE$programming model naturally fits keyword search algorithms, as they havedistinguished phases, to allow {\em preemptive} searches to mitigate stalenessin a distributed system. Finally, we investigate the performance andeffectiveness of $\DKWS$ through experiments using real-world datasets. We findthat $\DKWS$ is up to two orders of magnitude faster than related techniques,and its communication costs are $7.6$ times smaller than those of othertechniques.

DKWS: A Distributed System for Keyword Search on Massive Graphs (Complete Version)